A Dual-Engine Artificial Intelligence Framework Accelerates Sustainable Aviation Fuel Component Synthesis
Guo Tian, Honghao Chen, Runyu Jiang, Chenxi Zhang, Xi Ning Lu, Xiaonan Wang, Fei Wei
Abstract
feedstocks demands multifunctional catalysts whose performance arises from nonlinear, high-dimensional interactions─beyond single-descriptor design rules. Here we present a dual-engine artificial intelligence framework that couples closed-loop active learning with interpretable machine learning, demonstrated for syngas conversion to sustainable aviation fuel. The approach autonomously explores vast catalyst spaces while distilling human-interpretable principles. We identify previously unreported compositions and a general rule: on a stable spinel backbone, placing a d-block metal at the tetrahedral (A)-site and an early lanthanide at the octahedral (B)-site creates cooperative d-f interactions that enable π-back-donation into the π* orbitals of oxygenated intermediates, strengthening adsorption and lowering formation barriers to accelerate intermediate generation and C-C coupling to jet-range aromatics. Guided by this rule, active sites such as Zn-Ce/Sm, Fe-Pr/La, and Ni-Ce achieve >75% selectivity to jet-fuel-range aromatic hydrocarbons with high space-time yields. Overall, the dual-engine approach not only accelerates discovery but also yields transparent, experimentally validated design rules─a generalizable blueprint for interpretable, AI-enabled catalyst design in complex sustainable chemistries.